Determining the genetic structure of populations is becoming an increasingly important aspect of genetic studies. One of the
most frequently used methods is the calculation of F-statistics using an Analysis of Molecular Variance (AMOVA). However,
this has the drawback that the population hierarchy has to be known a priori. Therefore, the population structure is often
based on the results of a clustering analysis. Here I show how these two steps, clustering and calculation of F-statistics,
can be com- bined in a single analysis. I do this by showing how the AMOVA framework is theoretically related to the widely
used method of K-means clustering and can be used for the clustering of populations into groups. Simulations were used to
show that the method performed very well both under random mating and under nonrandom mating. However, when the migration
rates were high, the results were better under random mating than under predominant selfing or clonal reproduction. Two summary
statistics were tested for estimating the number of clusters. Overall, pseudo-F showed the better performance, but BIC is
better for detecting whether any significant structure is present. The results show that the AMOVA-based K-means clustering
is useful for clustering population genetic data. Programs to perform the clustering can be downloaded from www.patrickmeirmans.
com/software.

Disclaimer/Complaints regulations

If you believe that digital publication of certain material infringes any of your rights or (privacy) interests, please let
the Library know, stating your reasons. In case of a legitimate complaint, the Library will make the material inaccessible
and/or remove it from the website. Please Ask the Library, or send a letter to: Library of the University of Amsterdam, Secretariat, Singel 425, 1012 WP Amsterdam, The Netherlands.
You will be contacted as soon as possible.